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Related Concept Videos

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Related Experiment Video

Updated: Jul 30, 2025

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Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments.

Hongwen Yan1, Songrui Cai1, Qiangsheng Li1

  • 1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Plants (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

This study enhances daylily intelligent detection using an optimized YOLOv5s model, achieving high accuracy and speed in complex environments. The improved model is crucial for developing automated daylily picking equipment.

Keywords:
YOLOv5backbone networkcomplex environment in the fielddaylilyintelligent detection

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Intelligent detection is crucial for automated daylily picking.
  • Complex field conditions like occlusion and uneven lighting challenge current detection models.

Purpose of the Study:

  • To develop and optimize an intelligent detection model for daylily in challenging agricultural environments.
  • To improve the accuracy and efficiency of daylily detection for intelligent picking operations.

Main Methods:

  • Utilized the YOLOv5s model for daylily detection.
  • Optimized YOLOv5s network parameters (depth and width).
  • Integrated lightweight networks (Ghost, Transformer, MobileNetv3) to enhance the CSPDarknet backbone.

Main Results:

  • The original YOLOv5s model significantly outperformed YOLOv4, SSD, and Faster R-CNN in mean average precision (mAP).
  • Parameter optimization increased the original YOLOv5s mAP by 7.7%.
  • The Transformer-backboned YOLOv5s model achieved an mAP increase of 0.2% and a 69% inference speed boost over the parameter-optimized model, reaching 81.4% precision, 74.4% recall, 78.1% mAP, and 93 FPS.

Conclusions:

  • The optimized YOLOv5s model demonstrates robust performance for accurate and rapid daylily detection in complex field settings.
  • This research provides valuable data and experimental insights for the advancement of intelligent daylily harvesting equipment.